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BWIM aided damage detection in bridges using machine learning
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.ORCID iD: 0000-0002-5447-2068
2015 (English)In: Journal of Civil Structural Health Monitoring, ISSN 2190-5452, Vol. 5, no 5, 715-725 p.Article in journal (Refereed) PublishedText
Abstract [en]

In this study, a new, model-free damage detection method is proposed and validated on a simple numerical experiment. The proposed algorithm used vibration data (deck accelerations) and bridge weigh-in-motion data (load magnitude and position) to train a two-stage machine learning setup to classify the data into healthy or damaged. The proposed method is composed in its first stage of an artificial neural network and on the second stage of a gaussian process. The proposed method is applicable to railway bridges, since it takes advantage of the fact that vehicles of known axle configuration cross the bridge regularly, that normally only one train is on the bridge at a time and that the lateral positioning of the loads does not change. The novelty of the proposed algorithm is that it makes use of the data on the load’s position, magnitude and speed that can be obtained from a Bridge Weigh-in-Motion system to improve the accuracy of the damage detection algorithm.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2015. Vol. 5, no 5, 715-725 p.
Keyword [en]
Bridge WIM, Damage detection, Machine learning, Railway bridge, Structural health monitoring
National Category
Other Civil Engineering
Identifiers
URN: urn:nbn:se:kth:diva-181978DOI: 10.1007/s13349-015-0137-4ScopusID: 2-s2.0-84950988417OAI: oai:DiVA.org:kth-181978DiVA: diva2:902936
Note

QC 20160212

Available from: 2016-02-12 Created: 2016-02-11 Last updated: 2016-02-12Bibliographically approved

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Gonzalez, IgnacioKaroumi, Raid
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